The DOI matches its Crossref record, with a small difference in title or year; 36 of the 39 references it cites were found in a registry or an index, and 1 are not checked yet.
Adaptive encrypted traffic fingerprinting with bi-directional dependence
Open accessAbstract
Recently, network traffic analysis has been increasingly used in various applications including security, targeted advertisements, and network management. However, data encryption performed on network traffic poses a challenge to these analysis techniques. In this paper, we present a novel method to extract characteristics from encrypted traffic by utilizing data dependencies that occur over sequential transmissions of network packets. Furthermore, we explore the temporal nature of encrypted traffic and introduce an adaptive model that considers changes in data content over time. We evaluate our analysis on two packet encrypted applications: website fingerprinting and mobile application (app) fingerprinting. Our evaluation shows how the proposed approach outperforms previous works especially in the open-world scenario and when defense mechanisms are considered.
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“These methods highly rely on expert-designed features and have limited generalization ability.”
From ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022) · cites this paper as [1]
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Metadata
- Authors
- Al-Naami, K., Chandra, S., Mustafa, A., Khan, L., Lin, Z., Hamlen, K., & Thuraisingham, B.
- Affiliations
- The University of Texas at Dallas (US)
- Venue
- Proceedings of the 32nd Annual Conference on Computer Security Applications
- Publisher
- ACM
- Published
- 2016-12-05
- Pages
- 177-188
- ISSN
- 1063-9527 (ISSN-L)
- OpenAlex
- W2560100125
- Cited by
- 145
- References
- 39
- Citations by year
- 2026: 29 · 2025: 28 · 2024: 33 · 2023: 21 · 2022: 8 · 2021: 9 · 2020: 7 · 2019: 7 · 2018: 2 · 2017: 1
- FWCI
- 1.80
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Computer Security Applications Conference (publishedVersion)
- Funders
- National Security Agency (H98230-15-1-0271)
- National Science Foundation (1054629)
- The Air Force Office of Scientific Research (FA-9550-12-1-0077,FA9550-14-1-0173)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Information Systems › Digital and Cyber Forensics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- encrypted traffic analysis, traffic fingerprinting, website fingerprinting, adaptive models, defense mechanisms
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